Efficient extraction and integration of pharmacokinetic (PK) data from scientific literature is critical for informed decision-making in drug development. Prior knowledge of PK parameters, particularly from similar compounds, supports first-in-human dosing, parameter estimation, and compound screening, ultimately helping to reduce attrition in clinical trials. While recent natural language processing (NLP) efforts have focused on extracting PK data from unstructured text, these approaches often overlook more comprehensive PK information and essential contextual metadata, which are usually reported in tables. Despite the prevalence and value of these tables, no previous work has systematically addressed the automated extraction of PK data from them. This thesis presents a novel NLP pipeline for identifying, extracting, and structuring PK data from scientific tables. The work addresses a key gap by targeting tables as a rich and underutilised source of PK information. The thesis is structured around four main components. First, a classification system combining supervised learning and prompt-based approaches is developed to retrieve PK-relevant tables from full-text biomedical articles. Second, heuristic and neural named entity recognition approaches are designed to extract PK parameters and associated metadata from table cells, including dose, species, study population, route of administration, units, and other contextual qualifiers. Third, an entity linking pipeline, including rule-based and zero-shot methods, is developed to normalise extracted data to a standardised PK ontology. Finally, the full pipeline is utilised to construct a large-scale PK database from PubMed Open Access articles. The database is evaluated through systematic sampling and manual quality assessment, and proof-of-concept analyses demonstrate how the extracted data can be used to characterise literature-wide reporting trends and explore comparative pharmacological questions. The results of this thesis demonstrate that automated PK table mining is both feasible and scalable, significantly accelerating the curation of high-quality datasets for pharmacometrics modelling. This work presents new open-source annotated corpora, domain-specific NLP methodologies, and practical tools for structuring PK literature, thereby opening the door to scalable, data-driven approaches in early drug development.
The study proposes a blockchain-based framework to overcome the challenges of data silos, privacy risks, and interoperability limitations in pharmacovigilance systems, focusing on the refinement of adverse drug reaction (ADR) data collection, storage, and analysis. Incorporating blockchain's transparency, immutability, and security, the framework comprises four core components: a data collection layer for multi-source ADR reporting, a standardization layer for data integration and validation, a blockchain network layer for tamper-proof storage and secure sharing, and a data analysis layer for real-time risk detection and visualization. The framework's efficacy in drug safety monitoring and regulatory efficiency is exemplified by the MediLedger and Merck's SAP Pharma Blockchain Proof of Concept case studies. The proposed resolution shows remarkable potential for advancing global pharmacovigilance practices.
OBJECTIVE: To evaluate the attitudes of the parties involved in the system toward the new features and measure the potential benefits of introducing the use of blockchain and machine learning (ML) to strengthen the in-place methods for safely prescribing medication. The proposed blockchain will strengthen the security and privacy of the patient's prescription information shared in the network. Once the ePrescription is submitted, it is only available in read-only mode. This will ensure there is no alteration to the ePrescription information after submission. In addition, the blockchain will provide an improved tracking mechanism to ensure the originality of the ePrescription and that a prescriber can only submit an ePrescription with the patient's authorization. Lastly, before submitting an ePrescription, an ML algorithm will be used to detect any anomalies (eg, missing fields, misplaced information, or wrong dosage) in the ePrescription to ensure the safety of the prescribed medication for the patient. METHODS: The survey contains questions about the features introduced in the proposed ePrescription system to evaluate the security, privacy, reliability, and availability of the ePrescription information in the system. The study population is comprised of 284 respondents in the patient group, 39 respondents in the pharmacist group, and 27 respondents in the prescriber group, all of whom met the inclusion criteria. The response rate was 80% (226/284) in the patient group, 87% (34/39) in the pharmacist group, and 96% (26/27) in the prescriber group. KEY FINDINGS: The vast majority of the respondents in all groups had a positive attitude toward the proposed ePrescription system's security and privacy using blockchain technology, with 72% (163/226) in the patient group, 70.5% (24/34) in the pharmacist group, and 73% (19/26) in the prescriber group. Moreover, the majority of the respondents in the pharmacist (70%, 24/34) and prescriber (85%, 22/26) groups had a positive attitude toward using ML algorithms to generate alerts regarding prescribed medication to enhance the safety of medication prescribing and prevent medication errors. CONCLUSION: Our survey showed that a vast majority of respondents in all groups had positive attitudes toward using blockchain and ML algorithms to safely prescribe medications. However, a need for minor improvements regarding the proposed features was identified, and a post-implementation user study is needed to evaluate the proposed ePrescription system in depth.
Madison MilneāIves, Ching Lam, Najib Rehman, Raja Sharif Ā· 5 authors
BACKGROUND: Adverse drug event reporting is critical for ensuring patient safety; however, numbers of reports have been declining. There is a need for a more user-friendly reporting system and for a means of verifying reports that have been filed. OBJECTIVE: This project has 2 main objectives: (1) to identify the perceived benefits and barriers in the current reporting of adverse events by patients and health care providers and (2) to develop a distributed ledger infrastructure and user interface to collect and collate adverse event reports to create a comprehensive and interoperable database. METHODS: A review of the literature will be conducted to identify the strengths and limitations of the current UK adverse event reporting system (the Yellow Card System). If insufficient information is found in this review, a survey will be created to collect data from system users. The results of these investigations will be incorporated into the development of a mobile and web app for adverse event reporting. A digital infrastructure will be built using distributed ledger technology to provide a means of linking reports with existing pharmaceutical tracking systems. RESULTS: The key outputs of this project will be the development of a digital infrastructure, including a backend distributed ledger system and an app-based user interface. CONCLUSIONS: This infrastructure is expected to improve the accuracy and efficiency of adverse event reporting systems by enabling the monitoring of specific medicines or medical devices over their life course while protecting patients' personal health data. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/28616.
Madison MilneāIves, Ching Lam, Najib Rehman, Raja Sharif Ā· 5 authors
BACKGROUND Adverse drug event reporting is critical for ensuring patient safety; however, numbers of reports have been declining. There is a need for a more user-friendly reporting system and for a means of verifying reports that have been filed. OBJECTIVE This project has 2 main objectives: (1) to identify the perceived benefits and barriers in the current reporting of adverse events by patients and health care providers and (2) to develop a distributed ledger infrastructure and user interface to collect and collate adverse event reports to create a comprehensive and interoperable database. METHODS A review of the literature will be conducted to identify the strengths and limitations of the current UK adverse event reporting system (the Yellow Card System). If insufficient information is found in this review, a survey will be created to collect data from system users. The results of these investigations will be incorporated into the development of a mobile and web app for adverse event reporting. A digital infrastructure will be built using distributed ledger technology to provide a means of linking reports with existing pharmaceutical tracking systems. RESULTS The key outputs of this project will be the development of a digital infrastructure, including a backend distributed ledger system and an app-based user interface. CONCLUSIONS This infrastructure is expected to improve the accuracy and efficiency of adverse event reporting systems by enabling the monitoring of specific medicines or medical devices over their life course while protecting patientsā personal health data. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/28616
Ji Woong Kim, Ah Ra Lee, Min Gyu Kim, Il Kon Kim Ā· 5 authors
Medication errors are one of the problems to be solved not only in Korea but also across the world. To prevent a medication accident in advance, the comprehensive management of individual medication history is essential. Currently, in the hospital-centric Personal Health Record system, if the patient is prescribed medicines at multiple hospitals, he/she needs to go to the hospital where a medicine was prescribed to get the corresponding needs such as prescription or medical certification to record the prescription information on his/her own. Alternatively, the patient needs to record the prescription information on his/her own. This is very time consuming, inconvenient, and even worse, not reliable method because of input errors. Therefore, in this study, we developed Patient-centric medication history recording system using blockchain, which is directly capturing QR code printed on the envelop by drug store based on prescription. All the information are stored using the hash value of the data in a blockchain and it prevents the tampering of the data. In addition, this system adopted Fast Healthcare Interoperability Resources, which is the international health information exchange standard, as way to improve interoperability.
We present the first complete problem for SZK, the class of promise problems possessing statistical zero-knowledge proofs (against an honest verifier). The problem, called Statistical Difference, is to decide whether two efficiently samplable distributions are either statistically close or far apart. This gives a new characterization of SZK that makes no reference to interaction or zero knowledge .We propose the use of complete problems to unify and extend the study of statistical zero knowledge. To this end, we examine several consequences of our Completeness Theorem and its proof, such as:---A way to make every (honest-verifier) statistical zero-knowledge proof very communication efficient, with the prover sending only one bit to the verifier (to achieve soundness error 1/2).---Simpler proofs of many of the previously known results about statistical zero knowledge, such as the Fortnow and Aiello--HĪµstad upper bounds on the complexity of SZK and Okamoto's result that SZK is closed under complement.---Strong closure properties of SZK that amount to constructing statistical zero-knowledge proofs for complex assertions built out of simpler assertions already shown to be in SZK.---New results about the various measures of "knowledge complexity," including a collapse in the hierarchy corresponding to knowledge complexity in the "hint" sense.---Algorithms for manipulating the statistical difference between efficiently samplable distributions, including transformations that "polarize" and "reverse" the statistical relationship between a pair of distributions.